The understanding of human brain mechanisms has captured the imagination of scientists for ages. From the quantitative perspective, there is evidence that damages to brain structure affect brain function and, as a consequence, cognitive aspects. As there is evidence that brain structure might be affected by altered cognition. However, the complex interplay between the human brain and the mind remains still poorly understood. This fact has important clinical consequences, limiting applications devoted to the prevention and treatment of brain diseases. In the present thesis, we aim to enhance our understanding of human brain mechanisms by means of an integrated and data-driven approach, by adopting a systemic perspective and leveraging on tools from computational and network neuroscience. We successfully enhance the state of the art of computational neuroscience in several manners. Firstly, we inspect human cognition by focusing on the geometric exploration of concepts in the human mind to build new datadriven metrics to complement the neurological assessment and to confirm Alzheimer’s disease diagnosis. We formalize a new stochastic process, the potential-driven random walk, able to model the trade-off between exploitation and exploration of network structure, by accounting for local and global information, providing a flexible tool to span from random walk to shortestpath based navigation. Probing the interplay between brain structure and dynamics by means of its Von Neumann entropy, we develop a new framework for the multiscale analysis of the human connectome, which is effective for discerning between healthy conditions and Alzheimer’s disease. Finally, by integrating data from the human brain structural connectivity, its functional response errors as measured by Direct Electrical Stimulation and semantic selectivity, we propose a new procedure for mapping the human brain triadic nature, thus providing a model-oriented bridge between the human brain and mind. Besides shedding more light on human brain functioning, our findings offer original and promising clues to develop integrated biomarkers for Alzheimer’s disease detection, with the potential of extension for applications to other neurodegenerative diseases and psychiatric disorders.

Resting-state functional MRI(rs-fMRI) represents a powerful means to assess brain functional connectivity in healthy subjects and in neuropsychiatric patients. Aberrant functional connectivity has been observed in subjects affected by Alcohol Use Disorders (AUD) and other forms of substance dependence, a major health issue worldwide with limited treatment options. Despite intense investigation, the specific neuronal substrates involved and the functional implications of aberrant connectivity in these patients remain unknown. Moreover, it is unclear whether treatment can reverse these alterations, and normalize functional connectivity. Several methodological and conceptual questions in the analysis of functional connectivity are still open, and contribute to this uncertainty. Functional connectivity is defined in terms of correlated MR-signal fluctuations, and in-scanner patient motion and other nuisance signals can introduce spurious correlations, thus representing substantial confounding factors. At a more general level, understanding the effects of complex conditions, like AUD, on brain connectivity and their functional implications requires a deep comprehension of the brain organizational principles at multiple scales, a tremendous challenge that is at the heart of modern neuroscience. In this PhD dissertation I address some of the outstanding questions in the analysis and interpretation of aberrant functional connectivity in AUD. To this end, I have embraced the formalism of graph-theory, a powerful framework to assess the effects of alcohol abuse on the local and global topological organization of resting state connectivity. On the methodological side, I have investigated the effects of subject’s motion on the structure of resting state networks, and compared efficacy of different approaches to remove motion-related confounds. Moreover, I demonstrate the importance of network sparsification to remove spurious connections from the graph while maximizing the structural information that can be extracted from the system. Leveraging these methodological developments, I have evaluated functional alterations in different samples of AUD patients. In two independent studies, I demonstrated specific alterations in the topological organization of the insular cortex and subcortical basal structures in recently detoxified alcoholics. Interestingly, protracted abstinence appears to partially normalize functional connectivity, thus suggesting that alcohol-induced alterations in connectivity may be amenable to treatment. Based on these findings, I have studied the effects on brain functional networks of a putative novel treatment based on deep Transcranial Magnetic Stimulation (TMS). Specifically, I analyzed resting state connectivity in AUD patients subjected to repetitive TMS of the bilateral insula and of the anterior cingulate cortex (ACC), and demonstrated treatment-induced changes that may underlie the efficacy of this potential treatment in surrogate clinical read-outs.

Variability in the brain’s functional network connectivity is associated with differences in cognition. The degree to which brain networks flexibly reconfigure, or alternatively remain stable, can differ across regions of cortex, across time, and across individuals. The goal of this dissertation was to investigate how the brain’s functional network architecture is reconfigured to support abstract reasoning and rule learning. I proposed that flexibility within frontoparietal cortex, combined with a stable network core, is beneficial for effective reasoning and rule learning. Experiment One investigated the activation patterns and dynamic community structure of brain networks associated with shifting task demands during abstract reasoning. Twenty-seven subjects underwent fMRI scanning during resting state and during a subsequent abstract reasoning task. When quantifying network reconfiguration between resting and task states, I found a stable system within default and somatomotor networks alongside a more flexible frontoparietal control network. The results motivated a novel understanding of how the brain performs reasoning tasks: an underlying stable functional network acts as a cognitive control mechanism, priming task-active nodes within frontoparietal cortex to variably activate for unique task conditions. Experiment Two used a dynamic network analysis to identify changes in functional brain networks that were associated with context-dependent rule learning. During fMRI scanning, twenty-nine naïve subjects were challenged to learn a set of context-dependent rules. Successful learners showed greater stability in ventral attention and somatomotor regions, increased assortative mixing of cognitive control regions as rules were learned, and greater segregation of attention networks throughout the entire task. The results suggested that a stable ventral attention network and a flexible frontoparietal control network support sustained attention and the formation of rule representations. In Experiment Three, I carried out a separate analysis of data from Experiment 2 to characterize the functional connectivity patterns with the hippocampus that emerged during successful rule learning. The results demonstrated that the hippocampal head became increasingly functionally connected to the lateral frontal pole and caudate in successful learners. Additionally, the entire hippocampus exhibited decreased functional connectivity with the mid-cingulate and precuneus in successful learners. These three experiments demonstrated that stable functional connectivity in somatomotor and ventral attention networks, combined with flexible reconfiguration of frontoparietal cortex, is advantageous for successful rule learning and abstract reasoning. Altogether, this dissertation demonstrated that individual differences in dynamic functional connectivity are associated with learning, and that stability of brain networks across time and tasks supports higher order cognition. / 2025-01-23T00:00:00Z

<p>It was only in the last decade that Magnetic Resonance Imaging (MRI) technologies have achieved high-quality levels that enabled comprehensive assessments of individual human brain structure and functions. One of the most important advancements put forth by Thomas Yeo and colleagues in 2011 was the intrinsic functional connectivity MRI (fcMRI) networks which are highly reproducible and feature consistently across different individual brains. This dissertation aims to unravel different characteristics of human brain fcMRI networks, separately through network morphospace and collectively through stochastic block models.</p><p><br></p><p>The quantification of human brain functional (re-)configurations across varying cognitive demands remains an unresolved topic. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re-)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE). We use this framework to quantify the Network Configural Breadth across different tasks. Network configural breadth is shown to significantly predict behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence.</p><p><br></p><p>To properly estimate and assess whole-brain functional connectomes (FCs) is among one of the most challenging tasks in computational neuroscience. Among the steps in constructing large-scale brain networks, thresholding of statistically spurious edge(s) in FCs is the most critical. State-of-the-art thresholding methods are largely ad hoc. Meanwhile, a dominant proportion of the brain connectomics research relies heavily on using a priori set of highly-reproducible human brain functional sub-circuits (functional networks (FNs)) without properly considering whether a given FN is information-theoretically relevant with respect to a given FC. Leveraging recent theoretical developments in Stochastic block model (SBM), we first formally defined and subsequently quantified the level of information-theoretical prominence of a priori set of FNs across different subjects and fMRI task conditions for any given input FC. The main contribution of this work is to provide an automated thresholding method of individuals’ FCs based on prior knowledge of human brain functional sub-circuitry.</p>

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Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.

<div>The innovations in Magnetic Resonance Imaging (MRI) in the recent decades have given rise to large open-source datasets. MRI affords researchers the ability to look at both structure and function of the human brain. This dissertation will make use of one of these large open-source datasets, the Human Connectome Project (HCP), to study the structural and functional connectivity in the brain.</div><div>Communication processes within the human brain at different cognitive states are neither well understood nor completely characterized. We assess communication processes in the human connectome using ant colony-inspired cooperative learning algorithm, starting from a source with no <i>a priori</i> information about the network topology, and cooperatively searching for the target through a pheromone-inspired model. This framework relies on two parameters, namely <i>pheromone</i> and <i>edge perception</i>, to define the cognizance and subsequent behaviour of the ants on the network and the communication processes happening between source and target. Simulations with different configurations allow the identification of path-ensembles that are involved in the communication between node pairs. In order to assess the different communication regimes displayed on the simulations and their associations with functional connectivity, we introduce two network measurements, effective path-length and arrival rate. These measurements are tested as individual and combined descriptors of functional connectivity during different tasks. Finally, different communication regimes are found in different specialized functional networks. This framework may be used as a test-bed for different communication regimes on top of an underlying topology.</div><div>The assessment of brain <i>fingerprints</i> has emerged in the recent years as an important tool to study individual differences. Studies so far have mainly focused on connectivity fingerprints between different brain scans of the same individual. We extend the concept of brain connectivity fingerprints beyond test/retest and assess <i>fingerprint gradients</i> in young adults by developing an extension of the differential identifiability framework. To do so, we look at the similarity between not only the multiple scans of an individual (<i>subject fingerprint</i>), but also between the scans of monozygotic and dizygotic twins (<i>twin fingerprint</i>). We have carried out this analysis on the 8 fMRI conditions present in the Human Connectome Project -- Young Adult dataset, which we processed into functional connectomes (FCs) and time series parcellated according to the Schaefer Atlas scheme, which has multiple levels of resolution. Our differential identifiability results show that the fingerprint gradients based on genetic and environmental similarities are indeed present when comparing FCs for all parcellations and fMRI conditions. Importantly, only when assessing optimally reconstructed FCs, we fully uncover fingerprints present in higher resolution atlases. We also study the effect of scanning length on subject fingerprint of resting-state FCs to analyze the effect of scanning length and parcellation. In the pursuit of open science, we have also made available the processed and parcellated FCs and time series for all conditions for ~1200 subjects part of the HCP-YA dataset to the scientific community.</div><div>Lastly, we have estimated the effect of genetics and environment on the original and optimally reconstructed FC with an ACE model.</div>